Repository Analysis

supabase/realtime

Broadcast, Presence, and Postgres Changes via WebSockets

2.4 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of supabase/realtime, a Elixir project with 7,605 GitHub stars. SynthScan v2.0 examined 13,465 lines of code across 53 source files, recording 24 pattern matches distributed across 3 syntactic categories. The overall adjusted score of 2.4 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

2.4
Adjusted Score
2.4
Raw Score
100%
Time Factor
2026-07-14
Last Push
7.6K
Stars
Elixir
Language
13.5K
Lines of Code
53
Files
24
Pattern Hits
2026-07-14
Scan Date
0.00
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 0MEDIUM 8LOW 16

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 24 distinct pattern matches across 3 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Self-Referential Comments8 hits · 16 pts
SeverityFileLineSnippetContext
MEDIUM.github/workflows/lint.yml40 otp-version: 27.x # Define the OTP version [required]CODE
MEDIUM.github/workflows/lint.yml41 elixir-version: 1.18.x # Define the elixir version [required]CODE
MEDIUM.github/workflows/prod_linter.yml18 otp-version: 27.x # Define the OTP version [required]CODE
MEDIUM.github/workflows/prod_linter.yml19 elixir-version: 1.18.x # Define the elixir version [required]CODE
MEDIUM.github/workflows/forum_tests.yml34 otp-version: 27.x # Define the OTP version [required]CODE
MEDIUM.github/workflows/forum_tests.yml35 elixir-version: 1.18.x # Define the elixir version [required]CODE
MEDIUM.github/workflows/tests.yml66 otp-version: 27.x # Define the OTP version [required]CODE
MEDIUM.github/workflows/tests.yml67 elixir-version: 1.18.x # Define the elixir version [required]CODE
Over-Commented Block12 hits · 12 pts
SeverityFileLineSnippetContext
LOWtest/integration/tests.ts161// let topic = "topic:" + crypto.randomUUID();COMMENT
LOWtest/integration/tests.ts181//COMMENT
LOWtest/e2e/legacy/tests.ts181 // it("user using private channel for jwt connections can connect if they have enough permissions based on claims", asCOMMENT
LOWtest/e2e/supabase/config.toml61# If enabled, seeds the database after migrations during a db reset.COMMENT
LOWtest/e2e/supabase/config.toml101# Port to use for the email testing server web interface.COMMENT
LOWtest/e2e/supabase/config.toml161# Path to JWT signing key. DO NOT commit your signing keys file to git.COMMENT
LOWtest/e2e/supabase/config.toml181# Number of emails that can be sent per hour. Requires auth.email.smtp to be enabled.COMMENT
LOWtest/e2e/supabase/config.toml221# enabled = trueCOMMENT
LOWtest/e2e/supabase/config.toml241# Allow/disallow new user signups via SMS to your project.COMMENT
LOWtest/e2e/supabase/config.toml261# This hook runs before a new user is created and allows developers to reject the request based on the incoming user objCOMMENT
LOWpriv/static/robots.txt1# See https://www.robotstxt.org/robotstxt.html for documentation on how to use the robots.txt fileCOMMENT
LOWassets/js/app.js161 // "name":"realtime_presence_55",COMMENT
Hyper-Verbose Identifiers4 hits · 4 pts
SeverityFileLineSnippetContext
LOWtest/e2e/realtime-check.ts554async function runLoadPostgresChangesTests(testUser: { email: string; password: string }) {CODE
LOWtest/e2e/realtime-check.ts717async function runLoadBroadcastFromDbTests(testUser: { email: string; password: string }) {CODE
LOWtest/e2e/realtime-check.ts826async function runLoadBroadcastReplayTests(testUser: { email: string; password: string }) {CODE
LOWtest/e2e/realtime-check.ts1262async function runPostgresChangesFiltersTests(_testUser: { email: string; password: string }, supabase: SupabaseClient) CODE